Publications

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The goal of this study is to model the electrical response of gold plated copper electrical contacts exposed to a mixed flowing gas stream consisting of air containing 10 ppb H{sub 2}S at 30 C and a relative humidity of 70%. This environment accelerates the attack normally observed in a light industrial environment (essentially a simplified version of the Battelle Class 2 environment). Corrosion rates were quantified by measuring the corrosion site density, size distribution, and the macroscopic electrical resistance of the aged surface as a function of exposure time. A pore corrosion numerical model was used to predict both the growth of copper sulfide corrosion product which blooms through defects in the gold layer and the resulting electrical contact resistance of the aged surface. Assumptions about the distribution of defects in the noble metal plating and the mechanism for how corrosion blooms affect electrical contact resistance were needed to complete the numerical model. Comparisons are made to the experimentally observed number density of corrosion sites, the size distribution of corrosion product blooms, and the cumulative probability distribution of the electrical contact resistance. Experimentally, the bloom site density increases as a function of time, whereas the bloom size distribution remains relatively independent of time. These two effects are included in the numerical model by adding a corrosion initiation probability proportional to the surface area along with a probability for bloom-growth extinction proportional to the corrosion product bloom volume. The cumulative probability distribution of electrical resistance becomes skewed as exposure time increases. While the electrical contact resistance increases as a function of time for a fraction of the bloom population, the median value remains relatively unchanged. In order to model this behavior, the resistance calculated for large blooms has been weighted more heavily.

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Many conventional continuum approaches to solid mechanics do not address the size sensitivity of deformation to microstructural features like grain boundaries, and are therefore unable to capture much of the experimentally observed behavior of polycrystal deformation. We propose a non-local crystal plasticity model, in which the geometrically necessary dislocation (GND) density is calculated using a non-local integral approach. The model is based on augmented FeFp kinematics, which account for the initial microstructure (primarily grain boundaries) present in the polycrystal. With the augmented kinematics, the initial GND and the evolving GND state are determined in a consistent manner. The expanded kinematics and the non-local crystal plasticity model are used to simulate the tensile behavior in copper polycrystals with different grain sizes ranging from 14 μm to 244 μm. The simulation results show a grain size dependence on the polycrystal's yield strength, which are in good agreement with the experimental data. © 2007 Elsevier Ltd. All rights reserved.

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Frictional contact results in surface and subsurface damage that could influence the performance, aging, and reliability of moving mechanical assemblies. Changes in surface roughness, hardness, grain size and texture often occur during the initial run-in period, resulting in the evolution of subsurface layers with characteristic microstructural features that are different from those of the bulk. The objective of this LDRD funded research was to model friction-induced microstructures. In order to accomplish this objective, novel experimental techniques were developed to make friction measurements on single crystal surfaces along specific crystallographic surfaces. Focused ion beam techniques were used to prepare cross-sections of wear scars, and electron backscattered diffraction (EBSD) and TEM to understand the deformation, orientation changes, and recrystallization that are associated with sliding wear. The extent of subsurface deformation and the coefficient of friction were strongly dependent on the crystal orientation. These experimental observations and insights were used to develop and validate phenomenological models. A phenomenological model was developed to elucidate the relationships between deformation, microstructure formation, and friction during wear. The contact mechanics problem was described by well-known mathematical solutions for the stresses during sliding friction. Crystal plasticity theory was used to describe the evolution of dislocation content in the worn material, which in turn provided an estimate of the characteristic microstructural feature size as a function of the imposed strain. An analysis of grain boundary sliding in ultra-fine-grained material provided a mechanism for lubrication, and model predictions of the contribution of grain boundary sliding (relative to plastic deformation) to lubrication were in good qualitative agreement with experimental evidence. A nanomechanics-based approach has been developed for characterizing the mechanical response of wear surfaces. Coatings are often required to mitigate friction and wear. Amongst other factors, plastic deformation of the substrate determines the coating-substrate interface reliability. Finite element modeling has been applied to predict the plastic deformation for the specific case of diamond-like carbon (DLC) coated Ni alloy substrates.

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This report summarizes the work performed as part of a one-year LDRD project, 'Evolutionary Complexity for Protection of Critical Assets.' A brief introduction is given to the topics of genetic algorithms and genetic programming, followed by a discussion of relevant results obtained during the project's research, and finally the conclusions drawn from those results. The focus is on using genetic programming to evolve solutions for relatively simple algebraic equations as a prototype application for evolving complexity in computer codes. The results were obtained using the lil-gp genetic program, a C code for evolving solutions to user-defined problems and functions. These results suggest that genetic programs are not well-suited to evolving complexity for critical asset protection because they cannot efficiently evolve solutions to complex problems, and introduce unacceptable performance penalties into solutions for simple ones.

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